The formation of various integrated circuit (IC) structures on a wafer often relies on lithographic processes, sometimes referred to as photolithography, or simply lithography. As is well known, lithographic processes can be used to transfer a pattern of a photomask (also referred to herein as a mask or a reticle) to a wafer.
For instance, patterns can be formed from a photoresist layer disposed on the wafer by passing light energy through a mask having an arrangement to image the desired pattern onto the photoresist layer. As a result, the pattern is transferred to the photoresist layer. In areas where the photoresist is sufficiently exposed and after a development cycle, the photoresist material can become soluble such that it can be removed to selectively expose an underlying layer (e.g., a semiconductor layer, a metal or metal containing layer, a dielectric layer, a hard mask layer, etc.). Portions of the photoresist layer not exposed to a threshold amount of light energy will not be removed and serve to protect the underlying layer during further processing of the wafer (e.g., etching exposed portions of the underlying layer, implanting ions into the wafer, etc.). Thereafter, the remaining portions of the photoresist layer can be removed.
There is a pervasive trend in the art of IC fabrication to increase the density with which various structures are arranged. For example, feature size, line width, and the separation between features and lines are becoming increasingly smaller. For example, nodes with a critical dimension of about 45 nanometers (nm) to about 65 nm have been proposed. In these sub-micron processes, yield is affected by factors such as mask pattern fidelity, optical proximity effects and photoresist processing. Some of the more prevalent concerns include line end pullback, corner rounding and line-width variations. These concerns are largely dependent on local pattern density and topology.
Optical proximity correction (OPC) has been used to improve image fidelity. In general, current OPC techniques involve running a computer simulation that takes an initial data set having information relating the desired pattern and manipulates the data set to arrive at a corrected data set in an attempt to compensate for the above-mentioned concerns. The photomask can then be made in accordance with the corrected data set. Briefly, the OPC process can be governed by a set of geometrical rules (e.g., “rule-based OPC” employing fixed rules for geometric manipulation of the data set), a set of modeling principles (e.g., “model-based OPC” employing predetermined behavior data to drive geometric manipulation of the data set) or a hybrid combination of rule-based OPC and model-based OPC.
The computer simulation can involve iteratively refining the data set using an edge placement error (EPE) value as a benchmark for the compensating process. In some OPC processes, the features and lines of the desired (or target) pattern are broken into edge fragments (or edge segments) and each edge fragment is associated with a simulation point (also referred to as a control point). The fragmented data set is manipulated based on the rules and/or models. For example, the edge fragments can be moved inward or outward. Then, a simulation can be run to determine predicted placement of the edges by simulated “imaging” of the manipulated pattern onto a wafer. The predicted edges are compared against their desired placement; and, for a single point along each edge fragment such as the simulation point, a determination of how far the predicted edge placement deviates from the desired location is derived. If the predicted edge placement corresponds to the desired location, the edge placement error for that edge will be zero. As the predicted edge placement varies from the desired location, a positive or negative value in nanometers (or fractions thereof) can be derived. Determining EPE in this manner provides a one dimensional value for the offset between the desired edge (or segment thereof) and the predicted edge fragment placement.
To date, techniques for determining how the OPC engine fragments any particular feature or line is conducted in an intensively manual process that involves decisions made by a person or persons using engineering judgement. For instance, the OPC engine can be programmed with a static set of rules for setting fragmentation parameters for various features and lines of the data set. The fragmentation parameters can include, for example, the number of fragments, fragmentation length and simulation point placement for various types of features. Unfortunately, this rule based approach is not optimized for the wide variety of features and/or lines that may be present in a desired layout. Therefore, the fragmentation parameters for any particular feature or line may limit how well the data set can be corrected for the above-mentioned concerns.
Current OPC techniques work fairly well when the critical dimension is relatively large (e.g., 0.25 microns and larger). That is, using OPC with edge placement error as the driving factor, the corrected data set can become highly tuned. However, the applicants have found that as IC structures become smaller, correction of the pattern data set using conventionally fragmented data sets can lead to under-corrected and/or over-corrected patterns. As a result, undesired pattern irregularities may occur, such as excessive corner rounding, line end pull back, excessive feature and line width variations (e.g., including narrowing and/or bulging), shorting between structures and so forth.
Accordingly, there exists a need in the art for an improved methodology for fragmenting integrated circuit layout data sets prior to or part of an OPC simulation to correct a desired photolithographic pattern.